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    Predicting Three-Dimensional Dose Distribution of Prostate Volumetric Modulated Arc Therapy Using Deep Learning
    (2021-12-01)
    Kummanee, Patiparn
    ;
    Chancharoen, Wares
    ;
    Tangtisanon, Kanut
    ;
    Fuangrod, Todsaporn
    Background: Volumetric modulated arc therapy (VMAT) planning is a time-consuming process of radiation therapy. With a deep learning approach, 3D dose distribution can be predicted without the need for an actual dose calculation. This approach can accelerate the process by guiding and confirming the achievable dose distribution in order to reduce the replanning iterations while maintaining the plan quality. Methods: In this study, three dose distribution predictive models of VMAT for prostate cancer were developed, evaluated, and compared. Each model was designed with a different input data structure to train and test the model: (1) patient CT alone (PCT alone), (2) patient CT and generalized organ structure (PCTGOS), and (3) patient CT and specific organ structure (PCTSOS). The generative adversarial network (GAN) model was used as a core learning algorithm. The models were trained slice-by-slice using 46 VMAT plans for prostate cancer, and then used to predict and evaluate the dose distribution from 8 independent plans. Results: VMAT dose distribution was generated with a mean prediction time of approximately 3.5 s per patient, whereas the PCTSOS model was excluded due to a mean prediction time of approximately 17.5 s per patient. The highest average 3D gamma passing rate was 80.51 ± 5.94, while the lowest overall percentage difference of dose-volume histogram (DVH) parameters was 6.01 ± 5.44% for the prescription dose from the PCTGOS model. However, the PCTSOS model was the most reliable for the evaluation of multiple parameters. Conclusions: This dose prediction model could accelerate the iterative optimization process for the planning of VMAT treatment by guiding the planner with the desired dose distribution.
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    An effective classification procedure for diagnosis of prostate cancer in near infrared spectra
    (2010-05-01)
    Kim, Seoung Bum
    ;
    Temiyasathit, Chivalai
    ;
    Bensalah, Karim
    ;
    Tuncel, Altug
    ;
    Cadeddu, Jeffrey
    The main purpose of this study is to develop an effective classification procedure that discriminates between normal spectra and cancerous spectra in near infrared (NIR) spectroscopic data in which the classes are highly imbalanced and overlapped. Our proposed procedure consists of several steps. First, to ensure the comparability between spectra, normalization was done by dividing each spectral point by the area of the total intensity of the spectrum. Second, clustering analysis was performed with these normalized spectra to separate the spectra that represent the normal pattern from a mixed group that contains both normal and tumor spectra. Third, we conducted two-stage classification, the first being an effort to construct a classification model with the labels obtained from the preceding clustering analysis and the second being a classification to focus on the mixed group classified from the first classification model. To increase the accuracy, the second classification model was constructed based on the selected features that capture important characteristics of the spectral data. Our proposed procedure was evaluated by its classification ability in testing samples using a leave-one-out cross validation technique, yielding acceptable classification accuracy. © 2009 Elsevier Ltd. All rights reserved.